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import torch

from torch import Tensor


def compute_padding(in_h: int, in_w: int, *, out_h=None, out_w=None, min_div=1):
    """Returns tuples for padding and unpadding.

    Args:
        in_h: Input height.
        in_w: Input width.
        out_h: Output height.
        out_w: Output width.
        min_div: Length that output dimensions should be divisible by.
    """
    if out_h is None:
        out_h = (in_h + min_div - 1) // min_div * min_div
    if out_w is None:
        out_w = (in_w + min_div - 1) // min_div * min_div

    if out_h % min_div != 0 or out_w % min_div != 0:
        raise ValueError(
            f"Padded output height and width are not divisible by min_div={min_div}."
        )

    left = (out_w - in_w) // 2
    right = out_w - in_w - left
    top = (out_h - in_h) // 2
    bottom = out_h - in_h - top

    pad = (left, right, top, bottom)
    unpad = (-left, -right, -top, -bottom)

    return pad, unpad


def quantize_ste(x: Tensor) -> Tensor:
    """
    Rounding with non-zero gradients. Gradients are approximated by replacing
    the derivative by the identity function.

    Used in `"Lossy Image Compression with Compressive Autoencoders"
    <https://arxiv.org/abs/1703.00395>`_

    .. note::

        Implemented with the pytorch `detach()` reparametrization trick:

        `x_round = x_round - x.detach() + x`
    """
    return (torch.round(x) - x).detach() + x
